Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Experimental evaluation of GNSS and IMU fusion using gated recurrent unit

In this paper, a data-driven Inertial navigation systems (INS) and Global Navigation Satellite System (GNSS) fusion algorithm based on the use of the Gated Recur-rent Unit (GRU) is proposed. In this project, we trained the GRU neural network with Inertial Measurement Unit (IMU) raw data and GNSS Position, Velocity and Timing (PVT) solutions as input and the position difference between GNSS and ground truth as labels. Therefore, the trained model can estimate the rover’s positions by subtracting the predicted GNSS error from GNSS positions given IMU raw measurements and GNSS PVT solutions. To evaluate the performance of GNSS/INS fusion algorithms in realistic scenarios, we developed an experimental platform. Our experimental platform consists of a moving test rig and an external validation system. The moving test rig consists of a rover equipped with an LPMS-CU2: 9-Axis Inertial Measurement Unit (IMU) and U-Blox ZED-F9P GNSS receiver. For validation purposes, we employ an onboard real-time kinematic positioning (RTK)-GNSS receiver. The test scenarios include both open-sky and challenging conditions near buildings, which is beneficial for devolving and testing urban navigation systems. After training with collected experimental data in multiple test scenarios, the proposed algorithm is able to improve GNSS positioning accuracy by more than 60% for the open-sky environment and 30% for the urban environment

Real-time implementation of YOLO+JPDA for small scale UAV multiple object tracking

This paper describes the development of a real-time multiple object detection and tracking system for a small scale UAV. The YOLO deep learning visual object detection algorithm and JPDA multiple target detection algorithm, were selected and implemented. The theory and implementation details of these algorithms are presented. The performance analysis of the system is done on both public dataset and aerial videos taken by UAV

An inverse design paradigm of multi-functional elastic metasurface via data-driven machine learning

Elastic metasurfaces have become one of the most promising platforms for manipulating mechanical wavefronts with the striking feature of ultra-thin geometry. The conventional design of mechanical metasurfaces significantly relies on numerical, trial-and-error methods to identify structural parameters of the unit cells, which requires huge computational resources and could be extremely challenging if the metasurface is multi-functional. Machine learning technique provides another powerful tool for the design of multi-functional elastic metasurfaces because of its excellent capability in building nonlinear mapping relation between high-dimensional input data and output data. In this paper, a machine learning network is introduced to extract the complex relation between high-dimensional geometrical parameters of the metasurface unit and its high-dimensional dynamic properties. Based on a big dataset, the well-trained network can play the role of a surrogate model in the inverse design of a multi-functional elastic metasurface to significantly shorten the time for the design. Such method can be conveniently extended to design other multi-functional metasurfaces for the manipulation of optical, acoustical or mechanical waves.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Published versionThe authors would like to acknowledge funding support from the Ministry of Education Singapore under Grant No. MOE2019- T2-2-068, A*STAR Singapore Science and Engineering Research Council under AME Individual Research Grant (IRG) 2018 Grant Call (Project No. A1983c0030), and the funding support from National Natural Science Foundation of China (No. 12202117)

Abnormal Grain Growth: A Spontaneous Activation of Competing Grain Rotation

Unconventional white-beam Laue synchrotron X-ray diffraction is used on fine-grained, as-rolled magnesium alloy during an in situ heating experiment. At high temperatures, reflections of single grains are superimposed on the halo stemming from matrix grains. Some unique grain reflections spontaneously move, indicating grain rotations in response to torque expedited at grain boundaries. When a grain boundary spontaneously activates, it can begin to rotate, allowing diffusive mass transport and activating the boundaries of its other neighbors. Now the given grain can freely rotate toward coalescence; however, the multitude of grain boundaries compete in torque orientation and magnitude, resulting in zigzag rotations. After coalescence, the larger grain is still active and continues this scenario of growth, while the majority of the matrix grains remain inactive. The first-time experimental observation of such erratic grain behavior supplies the missing puzzlestone leading to anomalous grain growth, long postulated in literature. The method of white beam Laue diffraction on fine-grained polycrystalline materials delivers a novel experimental method to study the erratic behavior of grain reorientation, as requested long ago by the scientific community. Such findings apply to wide ranges of materials undergoing grain growth, creep, and superplasticity, including those in metal engineering, ceramics, and geophysical disciplines

Heat-induced structural changes in magnesium alloys AZ91 and AZ31 investigated by in situ synchrotron high-energy X-ray diffraction

Abstract: In situ time/temperature-resolved synchrotron high-energy X-ray diffraction is applied to study heat-mediated structural changes and phase transformations in rolled sheets of AZ91 and AZ31 magnesium alloys. Azimuthal diffraction intensities along the Debye–Scherrer rings (AT-plots) are used to obtain information on grain recovery and recrystallization temperatures as well as temperature-assisted grain rotations. The azimuthally integrated diffraction intensities, plotted as functions of the scattering vector (QT-plots), provide vital data on the temperature-dependent lattice parameters of the Mg/Al matrix and intermetallic precipitates, as well as on the evolution of the precipitates’ volume fraction. It was found that in AZ31, the main precipitates are of the AlMn type, which is rather stable in the investigated temperature range (up to 773 K). In contrast, in AZ91, the major intermetallic precipitates, Al12Mg17, undergo complete dissolution above 600 K. It is caused by the enhanced diffusion of Al into the Mg/Al matrix, which according to the Al–Mg phase diagram, can adopt more Al at elevated temperatures. This diffusion is revealed by the proportional diminishing of the matrix lattice parameter (chemical strain), allowing us to quantify the Al content in the matrix. Fast temperature-dependent manipulation with intermetallic content in the Mg/Al alloy can, in principle, be used for controlling its mechanical properties. Graphical Abstract: [Figure not available: see fulltext.]